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Open AccessJournal ArticleDOI

Evidence and Credibility: Full Bayesian Significance Test for Precise Hypotheses

Carlos Eduardo Pereira, +1 more
- 25 Oct 1999 - 
- Vol. 1, Iss: 4, pp 99-110
TLDR
A Bayesian measure of evidence for precise hypotheses is presented to give a Bayesian alternative to significance tests or, equivalently, to p-values.
Abstract
A Bayesian measure of evidence for precise hypotheses is presented. The inte n- tion is to give a Bayesian alternative to significance tests or, equivalently, to p-values. In fact, a set is defined in the parameter space and the posterior probability, its credibility, is evaluated. This set is the "Highest Posterior Density Region" that is "tangent" to the set that defines the null hypothesis. Our measure of evidence is the complement of the credibility of the "tangent" region.

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Citations
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Journal ArticleDOI

Bayesian Hypothesis Testing for Psychologists: A Tutorial on the Savage-Dickey Method.

TL;DR: Here it is drawn attention to the Savage-Dickey density ratio method, a method that can be used to compute the result of a Bayesian hypothesis test for nested models and under certain plausible restrictions on the parameter priors.
Journal ArticleDOI

Can a Significance Test Be Genuinely Bayesian

TL;DR: The Full Bayesian Signicance Test, FBST, is extensively reviewed and its test statistic, a genuine Bayesian measure of evidence, is discussed in detail.
Journal ArticleDOI

Default “Gunel and Dickey” Bayes factors for contingency tables

TL;DR: This work outlines an alternative, Bayes factor method to quantify the evidence for and against the hypothesis of independence in R×C contingency tables, and describes different sampling models for contingency tables.
Journal ArticleDOI

Bayesian evidence test for precise hypotheses

TL;DR: The full Bayesian significance test (FBST) for precise hypotheses is presented, with some illustrative applications in this paper, where the authors discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established.
References
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Book

Practical Methods of Optimization

TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.
Journal ArticleDOI

A statistical paradox

Book

Statistical Evidence: A Likelihood Paradigm

TL;DR: The first principle of the Law of Likelihood as discussed by the authors is that the strength of evidence is limited by the expectation of the researcher's expectation, and the importance of the evidence is determined by the test of significance.
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